


import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					List[Any]      =       logging.get_logger(__name__)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							DPTConfig(embedding_type='''hybrid'''							)

     if "large" in checkpoint_url:
          snake_case											=							10_24
          snake_case											=							40_96
          snake_case											=							24
          snake_case											=							16
          snake_case											=							[5, 11, 17, 23]
          snake_case											=							[2_56, 5_12, 10_24, 10_24]
          snake_case											=							(1, 3_84, 3_84)

     if "nyu" or "midas" in checkpoint_url:
          snake_case											=							7_68
          snake_case											=							[1, 1, 1, 0.5]
          snake_case											=							[2_56, 5_12, 7_68, 7_68]
          snake_case											=							1_50
          snake_case											=							16
          snake_case											=							(1, 3_84, 3_84)
          snake_case											=							False
          snake_case											=							'''project'''

     if "ade" in checkpoint_url:
          snake_case											=							True
          snake_case											=							7_68
          snake_case											=							[1, 1, 1, 0.5]
          snake_case											=							1_50
          snake_case											=							16
          snake_case											=							'''huggingface/label-files'''
          snake_case											=							'''ade20k-id2label.json'''
          snake_case											=							json.load(open(cached_download(hf_hub_url(UpperCamelCase_				,UpperCamelCase_				,repo_type='''dataset'''							)							)				,'''r'''							)							)
          snake_case											=							{int(UpperCamelCase_							): v for k, v in idalabel.items()}
          snake_case											=							idalabel
          snake_case											=							{v: k for k, v in idalabel.items()}
          snake_case											=							[1, 1_50, 4_80, 4_80]

     return config, expected_shape
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							['''pretrained.model.head.weight''', '''pretrained.model.head.bias''']
     for k in ignore_keys:
          state_dict.pop(UpperCamelCase_				,UpperCamelCase_							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     if (
         "pretrained.model" in name
         and "cls_token" not in name
         and "pos_embed" not in name
         and "patch_embed" not in name
     ):
          snake_case											=							name.replace('''pretrained.model'''				,'''dpt.encoder'''							)
     if "pretrained.model" in name:
          snake_case											=							name.replace('''pretrained.model'''				,'''dpt.embeddings'''							)
     if "patch_embed" in name:
          snake_case											=							name.replace('''patch_embed'''				,''''''							)
     if "pos_embed" in name:
          snake_case											=							name.replace('''pos_embed'''				,'''position_embeddings'''							)
     if "attn.proj" in name:
          snake_case											=							name.replace('''attn.proj'''				,'''attention.output.dense'''							)
     if "proj" in name and "project" not in name:
          snake_case											=							name.replace('''proj'''				,'''projection'''							)
     if "blocks" in name:
          snake_case											=							name.replace('''blocks'''				,'''layer'''							)
     if "mlp.fc1" in name:
          snake_case											=							name.replace('''mlp.fc1'''				,'''intermediate.dense'''							)
     if "mlp.fc2" in name:
          snake_case											=							name.replace('''mlp.fc2'''				,'''output.dense'''							)
     if "norm1" in name and "backbone" not in name:
          snake_case											=							name.replace('''norm1'''				,'''layernorm_before'''							)
     if "norm2" in name and "backbone" not in name:
          snake_case											=							name.replace('''norm2'''				,'''layernorm_after'''							)
     if "scratch.output_conv" in name:
          snake_case											=							name.replace('''scratch.output_conv'''				,'''head'''							)
     if "scratch" in name:
          snake_case											=							name.replace('''scratch'''				,'''neck'''							)
     if "layer1_rn" in name:
          snake_case											=							name.replace('''layer1_rn'''				,'''convs.0'''							)
     if "layer2_rn" in name:
          snake_case											=							name.replace('''layer2_rn'''				,'''convs.1'''							)
     if "layer3_rn" in name:
          snake_case											=							name.replace('''layer3_rn'''				,'''convs.2'''							)
     if "layer4_rn" in name:
          snake_case											=							name.replace('''layer4_rn'''				,'''convs.3'''							)
     if "refinenet" in name:
          snake_case											=							int(name[len('''neck.refinenet'''							) : len('''neck.refinenet'''							) + 1]							)
          # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
          snake_case											=							name.replace(F'''refinenet{layer_idx}'''				,F'''fusion_stage.layers.{abs(layer_idx-4							)}'''							)
     if "out_conv" in name:
          snake_case											=							name.replace('''out_conv'''				,'''projection'''							)
     if "resConfUnit1" in name:
          snake_case											=							name.replace('''resConfUnit1'''				,'''residual_layer1'''							)
     if "resConfUnit2" in name:
          snake_case											=							name.replace('''resConfUnit2'''				,'''residual_layer2'''							)
     if "conv1" in name:
          snake_case											=							name.replace('''conv1'''				,'''convolution1'''							)
     if "conv2" in name:
          snake_case											=							name.replace('''conv2'''				,'''convolution2'''							)
     # readout blocks
     if "pretrained.act_postprocess1.0.project.0" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess1.0.project.0'''				,'''neck.reassemble_stage.readout_projects.0.0'''							)
     if "pretrained.act_postprocess2.0.project.0" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess2.0.project.0'''				,'''neck.reassemble_stage.readout_projects.1.0'''							)
     if "pretrained.act_postprocess3.0.project.0" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess3.0.project.0'''				,'''neck.reassemble_stage.readout_projects.2.0'''							)
     if "pretrained.act_postprocess4.0.project.0" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess4.0.project.0'''				,'''neck.reassemble_stage.readout_projects.3.0'''							)

     # resize blocks
     if "pretrained.act_postprocess1.3" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess1.3'''				,'''neck.reassemble_stage.layers.0.projection'''							)
     if "pretrained.act_postprocess1.4" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess1.4'''				,'''neck.reassemble_stage.layers.0.resize'''							)
     if "pretrained.act_postprocess2.3" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess2.3'''				,'''neck.reassemble_stage.layers.1.projection'''							)
     if "pretrained.act_postprocess2.4" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess2.4'''				,'''neck.reassemble_stage.layers.1.resize'''							)
     if "pretrained.act_postprocess3.3" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess3.3'''				,'''neck.reassemble_stage.layers.2.projection'''							)
     if "pretrained.act_postprocess4.3" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess4.3'''				,'''neck.reassemble_stage.layers.3.projection'''							)
     if "pretrained.act_postprocess4.4" in name:
          snake_case											=							name.replace('''pretrained.act_postprocess4.4'''				,'''neck.reassemble_stage.layers.3.resize'''							)
     if "pretrained" in name:
          snake_case											=							name.replace('''pretrained'''				,'''dpt'''							)
     if "bn" in name:
          snake_case											=							name.replace('''bn'''				,'''batch_norm'''							)
     if "head" in name:
          snake_case											=							name.replace('''head'''				,'''head.head'''							)
     if "encoder.norm" in name:
          snake_case											=							name.replace('''encoder.norm'''				,'''layernorm'''							)
     if "auxlayer" in name:
          snake_case											=							name.replace('''auxlayer'''				,'''auxiliary_head.head'''							)
     if "backbone" in name:
          snake_case											=							name.replace('''backbone'''				,'''backbone.bit.encoder'''							)

     if ".." in name:
          snake_case											=							name.replace('''..'''				,'''.'''							)

     if "stem.conv" in name:
          snake_case											=							name.replace('''stem.conv'''				,'''bit.embedder.convolution'''							)
     if "blocks" in name:
          snake_case											=							name.replace('''blocks'''				,'''layers'''							)
     if "convolution" in name and "backbone" in name:
          snake_case											=							name.replace('''convolution'''				,'''conv'''							)
     if "layer" in name and "backbone" in name:
          snake_case											=							name.replace('''layer'''				,'''layers'''							)
     if "backbone.bit.encoder.bit" in name:
          snake_case											=							name.replace('''backbone.bit.encoder.bit'''				,'''backbone.bit'''							)
     if "embedder.conv" in name:
          snake_case											=							name.replace('''embedder.conv'''				,'''embedder.convolution'''							)
     if "backbone.bit.encoder.stem.norm" in name:
          snake_case											=							name.replace('''backbone.bit.encoder.stem.norm'''				,'''backbone.bit.embedder.norm'''							)
     return name
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     for i in range(config.num_hidden_layers							):
          # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
          snake_case											=							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.weight'''							)
          snake_case											=							state_dict.pop(F'''dpt.encoder.layer.{i}.attn.qkv.bias'''							)
          # next, add query, keys and values (in that order) to the state dict
          snake_case											=							in_proj_weight[: config.hidden_size, :]
          snake_case											=							in_proj_bias[: config.hidden_size]
          snake_case											=							in_proj_weight[
              config.hidden_size : config.hidden_size * 2, :
          ]
          snake_case											=							in_proj_bias[
              config.hidden_size : config.hidden_size * 2
          ]
          snake_case											=							in_proj_weight[
              -config.hidden_size :, :
          ]
          snake_case											=							in_proj_bias[-config.hidden_size :]
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case											=							Image.open(requests.get(UpperCamelCase_				,stream=UpperCamelCase_							).raw							)
     return im
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case					,    snake_case											=							get_dpt_config(UpperCamelCase_							)
     # load original state_dict from URL
     # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
     snake_case											=							torch.load(UpperCamelCase_				,map_location='''cpu'''							)
     # remove certain keys
     remove_ignore_keys_(UpperCamelCase_							)
     # rename keys
     for key in state_dict.copy().keys():
          snake_case											=							state_dict.pop(UpperCamelCase_							)
          snake_case											=							val
     # read in qkv matrices
     read_in_q_k_v(UpperCamelCase_				,UpperCamelCase_							)

     # load HuggingFace model
     snake_case											=							DPTForSemanticSegmentation(UpperCamelCase_							) if '''ade''' in checkpoint_url else DPTForDepthEstimation(UpperCamelCase_							)
     model.load_state_dict(UpperCamelCase_							)
     model.eval()

     # Check outputs on an image
     snake_case											=							4_80 if '''ade''' in checkpoint_url else 3_84
     snake_case											=							DPTImageProcessor(size=UpperCamelCase_							)

     snake_case											=							prepare_img()
     snake_case											=							image_processor(UpperCamelCase_				,return_tensors='''pt'''							)

     # forward pass
     snake_case											=							model(**UpperCamelCase_							).logits if '''ade''' in checkpoint_url else model(**UpperCamelCase_							).predicted_depth

     if show_prediction:
          snake_case											=							(
              torch.nn.functional.interpolate(
                  outputs.unsqueeze(1							)				,size=(image.size[1], image.size[0])				,mode='''bicubic'''				,align_corners=UpperCamelCase_				,)
              .squeeze()
              .cpu()
              .numpy()
          )

          Image.fromarray((prediction / prediction.max()) * 2_55							).show()

     if pytorch_dump_folder_path is not None:
          Path(UpperCamelCase_							).mkdir(exist_ok=UpperCamelCase_							)
          print(F'''Saving model to {pytorch_dump_folder_path}'''							)
          model.save_pretrained(UpperCamelCase_							)
          print(F'''Saving image processor to {pytorch_dump_folder_path}'''							)
          image_processor.save_pretrained(UpperCamelCase_							)

     if push_to_hub:
          model.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)
          image_processor.push_to_hub('''ybelkada/dpt-hybrid-midas'''							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					str      =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--checkpoint_url",
     default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
     type=str,
     help="URL of the original DPT checkpoint you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default=None,
     type=str,
     required=False,
     help="Path to the output PyTorch model directory.",
 )
 parser.add_argument(
     "--push_to_hub",
     action="store_true",
 )
 parser.add_argument(
     "--model_name",
     default="dpt-large",
     type=str,
     help="Name of the model, in case you're pushing to the hub.",
 )
 parser.add_argument(
     "--show_prediction",
     action="store_true",
 )

 _SCREAMING_SNAKE_CASE     :					Union[str, Any]      =       parser.parse_args()
 convert_dpt_checkpoint(
     args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
 )








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
 from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




